- Why visualizations rather than text
- General principles of visualization
- Grammar of graphics
17. 5. 2022
Source Ware (2012)
Source Ware (2012)
Source Medina (2014)
A key figure in the modern approach to visualization of information.
Chartjunk; data : ink ratio; data density; micro - macro reading.
Less is more. Graphs are meant to communicate information effectively, design is meant to support that goal, not obscure it.
Source Harford (2021)
“Above all else show the data.” (Eduard Tufte)
Source of funding [thousands CZK]
Financování dle zdroje, v tisících Kč
BEFORE
AFTER
BEFORE
AFTER
BEFORE
AFTER
BEFORE
AFTER
BEFORE
AFTER
Respect for how human cognition works.
ACTUAL PUBLICATION
SUGGESTION FOR IMPROVEMENT
Schwabish, J. A. (2014) An Economist’s Guide to Visualizing Data
ACTUAL PUBLICATION
SUGGESTION FOR IMPROVEMENT
Schwabish, J. A. (2014) An Economist’s Guide to Visualizing Data
Schwabish, J. A. (2014) An Economist’s Guide to Visualizing Data
May be useful for two categories
Source Financial Times
You decide what message visualization brings to the forefront. But you are also responsible for possible distortions or manipulations.
axis y in -20 mil. (top), in 0 (bottom)
Data from July 2021
Emphasis on data
Readability
Integrity
What makes a good visualization? Individual components…
Seven chart layers. Three required:
Data
Aesthetics - mapping information to color, shape, saturation, …
Geometry - graphic elements that represent data
Four “extra”:
Facets (small multiples)
Aggregated statistics (e.g. regression curve)
Coordinate editing (e.g. logarithmic scale)
Theme (theme) - chart design
## # A tibble: 6 x 8 ## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex ## <fct> <fct> <dbl> <dbl> <int> <int> <fct> ## 1 Adelie Torge~ 39.1 18.7 181 3750 male ## 2 Adelie Torge~ 39.5 17.4 186 3800 fema~ ## 3 Adelie Torge~ 40.3 18 195 3250 fema~ ## 4 Adelie Torge~ NA NA NA NA <NA> ## 5 Adelie Torge~ 36.7 19.3 193 3450 fema~ ## 6 Adelie Torge~ 39.3 20.6 190 3650 male ## # ... with 1 more variable: year <int>
ggplot(data = penguins)
ggplot(data = penguins,
aes(x = sex))
ggplot(data = penguins,
aes(x = sex)) +
geom_bar()
ggplot(data = penguins %>%
filter(!is.na(sex)),
aes(x = sex,
y = bill_length_mm)) +
geom_boxplot() +
theme_classic()
This presentation naturally draws on a hard-to-imagine volume of work of a hard-to-imagine number of people.
Nevertheless, I would especially like to thank Petr Bouchal. With him, in 2016, we prepared a course on the methodology of science at the summer academy for high school students Discover, where we devoted a lot of space to visualization. Petr was also a guest lecturer in my courses at Faculty of Arts, CU, and it was only during his lectures that I fully appreciated the value of seeing visualization as a full-fledged auxiliary scientific discipline. I became acquainted with a number of examples in this presentation thanks to Petr
If the resources referenced in the presentation are not interactive (they do not contain a link directly to their location), you can find them in the list here:
Harford, Tim. 2021. How to Make the World Add up: Ten Rules for Thinking Differently about Numbers. 1st edition. London: The Bridge Street Press.
Medina, John. 2014. Brain Rules (Updated and Expanded): 12 Principles for Surviving and Thriving at Work, Home, and School. Second edition. Seattle, WA: Pear Press.
Ware, Colin. 2012. Information Visualization: Perception for Design. 3rd edition. Waltham, MA: Morgan Kaufmann.